REPOGEO REPORT · LITE
huggingface/evaluate
Default branch main · commit a7dd3383 · scanned 6/26/2026, 11:47:11 AM
GitHub: 2,457 stars · 321 forks
Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.
3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).
Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface huggingface/evaluate, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.
Action plan — copy-paste fixes
3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.
- highreadme#1Reposition the README's core value proposition to the top
Why:
CURRENT> **Tip:** For more recent evaluation approaches, for example for evaluating LLMs, we recommend our newer and more actively maintained library LightEval.
COPY-PASTE FIX🤗 Evaluate is the standardized library for easily evaluating machine learning models and datasets across various tasks and frameworks. It provides dozens of popular metrics, tools for model comparison, and methods for dataset measurement, all ready to use with a simple command like `accuracy = load("accuracy")`. - mediumtopics#2Add more specific topics to improve categorization
Why:
CURRENTevaluation, machine-learning
COPY-PASTE FIXevaluation, machine-learning, ml-metrics, model-evaluation, dataset-evaluation, nlp, computer-vision
- lowreadme#3Add a 'Comparison with other tools' section to the README
Why:
COPY-PASTE FIX## Comparison with other tools While tools like MLflow, Weights & Biases, and scikit-learn offer broader machine learning lifecycle management or general-purpose utilities, 🤗 Evaluate focuses specifically on providing a standardized, framework-agnostic library for accessing and computing a wide array of evaluation metrics. It complements these tools by offering a dedicated hub for metrics, rather than replacing their core functionalities like experiment tracking or model training.
Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash
Category visibility — the real GEO test
Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?
Same questions for every model — switch tabs to compare answers and rankings.
- MLflow · recommended 2×
- scikit-learn · recommended 1×
- Optuna · recommended 1×
- TensorFlow / Keras · recommended 1×
- PyTorch Lightning · recommended 1×
- CATEGORY QUERYWhat's a good library for standardizing machine learning model evaluation and accessing common metrics?you: not recommendedAI recommended (in order):
- scikit-learn
- MLflow
- Optuna
- TensorFlow / Keras
- PyTorch Lightning
- Evidently AI
- Deepchecks
AI recommended 7 alternatives but never named huggingface/evaluate. This is the gap to close.
Show full AI answer
- CATEGORY QUERYHow can I easily compare machine learning model performance across different frameworks like PyTorch and TensorFlow?you: not recommendedAI recommended (in order):
- MLflow
- TensorBoard
- Weights & Biases
- Neptune.ai
- Pandas
- Matplotlib
- Seaborn
- DVC
- DVC Studio
AI recommended 9 alternatives but never named huggingface/evaluate. This is the gap to close.
Show full AI answer
Objective checks
Rule-based audits of metadata signals AI engines weight most.
- Metadata completenesspass
- README presencepass
Self-mention check
Does AI even know your repo exists when asked about it directly?
- Compared to common alternatives in this category, what is the core differentiator of huggingface/evaluate?passAI named huggingface/evaluate explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- If a team adopts huggingface/evaluate in production, what risks or prerequisites should they evaluate first?passAI named huggingface/evaluate explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
- In one sentence, what problem does the repo huggingface/evaluate solve, and who is the primary audience?passAI named huggingface/evaluate explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
Embed your GEO score
Drop this badge into the README of huggingface/evaluate. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.
Live preview[](https://repogeo.com/en/r/huggingface/evaluate)<a href="https://repogeo.com/en/r/huggingface/evaluate"><img src="https://repogeo.com/badge/huggingface/evaluate.svg" alt="RepoGEO" /></a>Subscribe to Pro for deep diagnoses
huggingface/evaluate — Lite scans stay free; this card itemizes Pro deep limits vs Lite.
- Deep reports10 / month
- Brand-free category queries5 vs 2 in Lite
- Prioritized action items8 vs 3 in Lite